![]() SilverStone has provided the following in the box: The maximum rated speed on these fans is 2200 RPM generating a massive airflow of 120 CFM using 4mmH₂O static pressure. Salient specifications are listed over here. Shark Force 140 is printed over here showing the model of this unit. Printing is done right over this cardboard packing box. Price: USD 27.36įans are shipped inside a brown color cardboard box. These fans feature a daisy-chain cabling system which simplifies cable management. These are performance enhanced 140mm PWM fans having a maximum airflow rating of 140CFM and air pressure rating of 4mmH₂O. This is a dedicated review of these fans. SilverStone has joined our coming-up comparative performance of 140mm fans on ALPHACOOL SuperNova 1260mm radiator and sent us 20x SF140 fans for this purpose. This time we are taking a look at Shark Force SF140 fans. These are available in 120mm and 140mm sizes. ![]() As part of OpenXLA, we’ll work closely with our community to carry this momentum forward.SilverStone recently released their next generation, high-performance edition fans named Shark Force (SF). That was a key reason I started IREE and was ultimately behind the decision to become part of the OpenXLA project. Beyond the numbers, I am really proud that we were able to create an engaged community that is empowered to make this kind of project happen. It was fantastic to see the Nod/AMD collaboration produce the great results it has. ![]() OpenXLA aims to let ML developers build models in their preferred framework (TensorFlow, PyTorch, JAX) and easily execute them with high performance across a wide range of hardware backends (GPU, CPU, and ML accelerators). IREE is part of the OpenXLA Project, an ecosystem of ML compiler and infrastructure technologies being co-developed by AI/ML industry leaders including AMD, Google, Nod.ai and many more. SHARK is an open source cross platform (Windows, macOS and Linux) Machine Learning Distribution packaged with torch-mlir (for seamless PyTorch integration), LLVM/MLIR for re-targetable compiler technologies along with IREE (for efficient codegen, compilation and runtime) and Nod.ai’s tuning. We are not done with performance, ease of use or feature requests – so stay tuned for more over the upcoming weeks. Give it a try at, share and show off what you can create with Generative AI. User generated content from nod.ai Discord channel Here are images generated by the SHARK community on AMD RDNA™ architecture-based devices in the #ai-art Discord channel. The community has reported that it is able to run on older generation hardware dating back five years. Today you can download a single file and get started on your Generative AI endeavor. So we made our Stable Diffusion WebGUI easily accessible and usable. We believe that Generative AI should be accessible to everyone irrespective of their technical background. We’re very excited to see Nod.ai porting the Stable Diffusion model to run performantly on AMD’s RDNA3 architecture, in collaboration with the MLIR, IREE and OpenXLA community. ![]() Nod.ai has been optimizing this state-of-the-art model to generate Stable Diffusion images, using 50 steps with FP16 precision and negligible accuracy degradation, in a matter of seconds. The Nod.ai team is pleased to announce Stable Diffusion image generation accelerated on the AMD RDNA™ 3 architecture running on this beta driver from AMD. There has also been a wide variety of accuracy-degrading performance optimizations like Xformers and Flash Attention, which are great tools if you are open to trading accuracy for performance, however we wanted to unlock maximum performance without any of the accuracy degrading optimizations. The fastest generally available solutions on Windows start at 5 seconds or higher unless you want to start copying DLLs by hand to upgrade the torch libraries. Generative AI has taken the world by storm but until now it took a while to generate an image from a text prompt with the typical 50 Steps on a GPU. Come check it out at The Venetian – Titian 2304 At CES 2023, we are showing our Stable Diffusion Demonstration on Radeon™ RX 7900 XTX, in the AMD booth.
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